The operational scale of edge computing introduces new challenges for building and operating suitable computation platforms. This talk was given at SEC'18 (http://acm-ieee-sec.org/2018/) and reports on the paper 'Portable Energy-Aware Cluster-Based Edge Computers'.

1. Portable Energy-Aware Cluster-Based
Edge Computers
Thomas Rausch, Cosmin Avasalcai, Schahram Dustdar
TU Wien, Vienna Austria
Distributed Systems Group
http://dsg.tuwien.ac.at
ACM/IEEE Symposium on Edge Computing
2018, Bellevue, WA

2. 2
Edge Computers
Cloudlet
Cloud
Server Computer
Edge Computer
Extension to the Edge

3. 3
Cloudlets for Fieldwork Scenarios
Edge CloudIoT
Lewis et al., 2014. “Tactical cloudlets: Moving cloud computing to the edge”
Edge Computer Requirements
● Performance
● Portable
● Energy-Efficient
● Reliable
Edge Computer Requirements
● Performance
● Portable
● Energy-Efficient
● Reliable

4. 4
Cluster-Based Edge Resources?
Sun Modular Datacenter Ubuntu Orange Box
(Intel NUC cluster)
1
Elkhatib et al., 2017, “On Using Micro-Clouds to Deliver the Fog”
“Micro Clouds” 1
Server Computers SOC & Single Board Computers

5. 5
Cluster-Based Edge Computer Prototype
Motherboard ASUS P10S-I Mini-ITX
CPU Intel Xeon E3-1230 (4 cores + HT)
RAM 2x16GB Kingston HyperX Fury
SSD Intel SSD 600p 128 GB M.2.
PSU picoPSU-90 12V

6. 6
Energy-Aware Clustered Edge Computers
1
13
3
2
2
4
4

7. 7
Examine Cluster Configurations
● Resource Utilization?
● Energy Consumption?
● System Responsiveness?
SqueezeNet
MXNet Model Server

8. 8
Energy Signatures of Node Operations
Offline: 2 W
Shutdown: 4-6 s
~620 J
Boot (WoL)
Docker container
with MXNet starts
Average Idle: 9 W
Boot: 45-48 s
~39 J
E(idle(t )) = E(boot) + E(shutdown)
t = ~110 s
Boot Cycle

9. 9
∑(E(ni)) 17.0 Wh 19.4 Wh 19.1 Wh 19.3 Wh
n1
n2
RTT
.99
.95
μ
CPU
n1
: 100%
n2
: off
n3
: off
n4
: off
n1
: 90%
n2
: 10%
n3
: off
n4
: off
n1
: 80%
n2
: 20%
n3
: off
n4
: off
n1
: 70%
n2
: 30%
n3
: off
n4
: off
300r/s

10. 10
∑(E(ni)) 19.4 Wh 19.4 Wh 19.4 Wh 21.5 Wh
n1
: 60%
n2
: 40%
n3
: off
n4
: off
n1
: 50%
n2
: 50%
n3
: off
n4
: off
n1
: 33%
n2
: 33%
n3
: 33%
n4
: off
n1
: 25%
n2
: 25%
n3
: 25%
n4
: 25%

11. 11
Conventional Wisdom
[R]ecent studies show the CPU
utilization has a linear relationship on
power consumption, when dynamic
voltage and frequency scaling is
applied.
[R]ecent studies show the CPU
utilization has a linear relationship on
power consumption, when dynamic
voltage and frequency scaling is
applied.
Farahnakian et al., 2014. Energy-Efficient Virtual Machines
Consolidation in Cloud Data Centers Using Reinforcement
Learning
Kusic et al., 2009. Power and performance management of
virtualized computing environments via lookahead control
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
0
20
40
60
80
100
120
140
160
HP ProLiant G5 HP ProLiant G4
CPU (%)
W

12. 12
Intricacies of Power Management
CPU %
Freq (MHz)
Power (W)
RTT
Segmented relation

13. 13
Workload Centric View
Questions that arise
● How to cooperate with hardware?
● Pareto optimality energy vs. responsiveness?
● How to measure for multi-tenancy?
Frequency
1.0
3.3.5
GHz

14. 14
Dipl.-Ing. (MSc), BSc
Thomas Rausch
Research Assistant
TU Wien
Information Systems Engineering
Argentinierstrasse 8-194-02, Vienna, Austria
T: +43 1 58801-184838
E: trausch@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/trausch